Mutational Signature Analysis

Day 4 - Cancer Genome Analysis - Latin America and the Caribbean

Background

The goal of this hands-on practical session is to perform a mutational signature analysis of a set of cancer patients and decipher the biological processes underlying the mutations found in the patients’ tumors, and ultimately leading to the development and progression of the disease.

While the analysis of the somatic mutations found in a tumor is commonly done looking for potential driver alterations (defined as those leading to cancer development, and previously reviewed in the course), in the last decade, mutational signature analysis has revolutionized this paradigm. By exploring and characterizing the patterns of all somatic mutations (also including passenger mutations), it is possible to identify the causes leading to a specific tumor, including different sources like environmental exposures (UV light exposure) or lifestyle choices (tobacco smoking), as well as endogenous cellular mechanisms (like a deficient DNA repair machinery). Somatic mutations are present in all cells of the human body and occur throughout life. They are the consequence of multiple mutational processes, which generate unique combinations of mutation types, termed mutational signatures.

To perform mutational signature analysis, we will be using the SigProfiler suite of tools. SigProfiler provides a comprehensive and integrated suite of bioinformatic tools for performing mutational signature analysis. The software covers the analytical lifecycle starting with the generation of the mutational matrices (SigProfilerMatrixGenerator) and finishing with signature extraction (SigProfilerExtractor) and assignment (SigProfilerAssignment), as well as supporting functionality for plotting and simulation.

1. Exploring a cancer genomics data portal

Our input data will be cancer sequencing data from the publicly available cBioPortal platform. In particular, we will analyze data from The Cancer Genome Atlas (TCGA) project. To focus on this particular project and, specifically, on the latest published results, you should search for “tcga pancancer atlas” in the cBioPortal Query tab, as shown below.

We will perform this exercise using data from colorectal adenocarcinoma patients, and jointly discuss the results together at the end of the session. The goal is to identify the shared patterns of mutations for this specific cancer type, as well as the most common mutational signatures active.

Side-note: For the upcoming group projects session, we will also be using data from the cBioPortal platform, so make sure to spend some time getting familiar with the platform, as well as exploring all the metadata available for the different studies and cancer types.

After selecting the data set of choice, we will choose the Explore selected studies button. This will lead us to the Summary panel, where different clinical features can be explored.

Discussion #1

Please explore the clinical and molecular data categories present in cBioPortal and answer the questions below.

Side-note: Please provide your answers in the red rectangles after each question. Correct answers will be indicated by a change of rectangle color.

• Q1. How many cancer samples are included in the dataset?
• Q2. Which is the most mutated gene?
• Q3. Which is the most common treatment undergone by patients? ”

More details

Note: cancer samples are not the same as cancer patients, as more than one sample can come from the same patient, e.g., because the patient had multiple tumors or there are various biopsies from the same tumor (at different time points or locations).

2. Downloading cancer sequencing data

The data to use for the exercise has already been downloaded and it’s present in the virtual machine. In case you want to download any cancer sequencing data from cBioPortal in the future for your own analysis, you should use the following button within the Summary panel.

Side-note: The code presented below was built to run the analysis on the colorectal adenocarcinomas cohort. In case you want to perform mutational analysis in other cohorts or for your own projects, you will have to adapt it for your specific analysis.

This will download all available data for the cancer data set, including clinical and molecular data. In our case, the file of interest is the one containing the mutation data, which is named data_mutations.txt. Although the extension of this file is .txt, it was actually generated using the MAF format, commonly seen in cancer sequencing data and previously reviewed in the course.

2.1 Input data reformatting

Unfortunately, the MAF format used by cBioPortal is slightly different from the one developed by the National Cancer Institute, which is the default supported by the SigProfiler suite of tools. Considering this, we will reformat the file using the tidyverse collection of packages as follows:

# Load tidyverse collection of packages
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

# Read cBioPortal style MAF file
maf_cbioportal = read.delim('coadread_tcga_pan_can_atlas_2018/data_mutations.txt')

# Selection of specific columns needed by SigProfiler
maf_sp = maf_cbioportal %>%
  select(Hugo_Symbol, Entrez_Gene_Id, Center, NCBI_Build, Chromosome,
         Start_Position, End_Position, Strand, Variant_Classification,
         Variant_Type, Reference_Allele, Tumor_Seq_Allele1,
         Tumor_Seq_Allele2, dbSNP_RS, dbSNP_Val_Status, Tumor_Sample_Barcode)

# Filter for only considering single base substitutions
maf_sp = maf_sp %>%
    filter(Variant_Type == 'SNP')

We will also create a specific folder for all our signature analysis results, as well as a specific folder inside for the updated MAF file, as this is also required by the SigProfiler tools.

# Create new folder for signature analysis results
dir.create('signatures')
# Create new folder for updated MAF file (needed for SigProfilerMatrixGenerator)
dir.create('signatures/SPMG/')

# Write updated MAF file
write.table(maf_sp, 'signatures/SPMG/data_mutations.maf', quote = F,
            row.names = F, sep = '\t')

3. Generating mutational matrices

Benefiting from the standard MAF format file we have generated, we can now use the SigProfilerMatrixGenerator package to manage this sequencing data. This package allows us to create a matrix classifying the mutations in the MAF file into the subtypes of interest. Mutational matrices are the first step for mutational signature analysis and correspond to a helpful data type, as they contain no protected information.

In this case, we are going to focus on the SBS96 mutational context, which, as mentioned in the lecture, allows classifying single base substitutions in different categories based on the mutated nucleotide, as well as the immediately preceding and posterior nucleotides to the mutation (a.k.a. the 5’ and 3’ nucleotides from the mutation). Other contexts exist for single base substitutions, as well as additional variant types, such as doublet base substitutions (DBSs) or short insertions and deletions (indels; IDs).

Although SigProfilerMatrixGenerator and the other SigProfiler tools that we will be using today have been developed in Python, there are R wrappers available. These make it easy to switch between the two different platforms. In the context of this course, we will use the R version of all SigProfiler tools (note the final R added to the name of the packages). More information can be found on the associated GitHub pages for the python and R versions of SigProfilerMatrixGenerator, as well as in the corresponding wiki page.

Side-note: Although the SigProfiler suite of tools has already been pre-installed in the virtual machine for this course, if you are interested in applying mutational signature analysis in your own projects, please take a look at the optional section at the end of this document for installation instructions.

# Load reticulate library (for using python packages in R)
library(reticulate)

# Fixing conda environment (check optional section for details)
use_condaenv('mutational_signatures')

# Load R wrapper package for SigProfilerMatrixGenerator
library(SigProfilerMatrixGeneratorR)

The first step to run SigProfilerMatrixGenerator is installing a reference genome, that should match the one used for the alignment of the next generation sequencing data. We have already preinstalled human reference genomes GRCh37 and GRCh38 in the virtual machines, but in case you need to install these genomes (or different ones) on a different computer you can follow the code below:

# Install reference genome (only required once, previously done in the VM)
install('GRCh37', rsync=FALSE, bash=TRUE)

In order to run SigProfilerMatrixGenerator for the colorectal adenocarcinoma tumors from the TCGA project available in cBioPortal you can use the following:

# Generate mutational profiles analysis using SigProfilerMatrixGenerator
matrices <- SigProfilerMatrixGeneratorR(project = "COREAD",
                                        genome = "GRCh37",
                                        matrix_path = "./signatures/SPMG",
                                        plot = F,
                                        exome = T)

A successful run will indicate that mutational matrices have been generated for a total of 528 samples and 305,525 single base substitutions. Please review the output files generated by SigProfilerMatrixGenerator by exploring the folder structure created inside the SPMG directory.

4. Visualizing mutational profiles

For the visualization of SBS96 mutational profiles, we will make use of the SigProfilerPlotting tool. To generate mutational profile plots, we will use the previously generated mutational matrices as input.

Side-note: Although SigProfilerMatrixGenertor also allows users to directly generate mutational profile plots, this is quite time consuming, as plots are generated for every different SBS classification (as well as for every sample).

library(SigProfilerPlottingR)
plotSBS(matrix_path = 'signatures/SPMG/output/SBS/COREAD.SBS96.exome',
        output_path = 'signatures/SPMG/output/SBS/',
        project = 'COREAD',
        plot_type = '96',
        percentage = FALSE)

The file containing the mutational profiles for all samples will be located in the directory specified in the output_path parameter. The mutational profiles for the first three samples of the cohort are provided as example below:

4.1 Average mutational profiles

To get an idea of the overall patterns of mutations in the whole cohort, it is useful to generate the average mutational profile of all samples. To do this, it’s important to keep in mind that we need to first obtain the relative mutational matrix, using percentages instead of absolute values. This step is required to avoid samples with high numbers of mutations to bias the average mutational profile.

Side-note: You can try to see what happens if you generate the average mutational profile directly with the absolute values instead of the relative ones. Can you observe any significant difference? Why do you think this happens?

# Generate average mutational profiles
mut_matrix = matrices[['96']]

# Get relative mutational matrix
relative_mut_matrix = apply(mut_matrix, 2, prop.table)

# Get average mutational matrix
average_mut_matrix = rowMeans(relative_mut_matrix)
average_mut_matrix = data.frame(Average_COREAD = average_mut_matrix)

# Add row names as column and print
average_mut_matrix_to_print = cbind(rownames(average_mut_matrix),
                                    average_mut_matrix)
colnames(average_mut_matrix_to_print)[1] = 'MutationType'
write.table(average_mut_matrix_to_print, 'signatures/avg_COREAD.SBS96.all',
            quote = F, row.names = F, sep = '\t')

# Plot average mutational profiles (note the percentage parameter now)
plotSBS(matrix_path = 'signatures/avg_COREAD.SBS96.all',
        output_path = 'signatures/',
        project = 'avg_COREAD',
        plot_type = '96',
        percentage = TRUE)

4.2 Average mutational profiles per subgroup

An interesting analysis when working with large cohorts of cancer cases is to identify differences according to specific subgroups based on clinical characteristics. In the case of colorectal cancer, there is several molecular subgroups that have been previously identified to have significant differences in the number and pattern of mutations (Muzny et al. 2012 Nature).

This have been annotated for the cBioPortal data using the Subtype column in the clinical metadata file provided. However, to start working with this metadata, we need to first filter it to use only the samples where we have mutation information.

# Read clinical file with metadata
metadata = read.delim('coadread_tcga_pan_can_atlas_2018/coadread_tcga_pan_can_atlas_2018_clinical_data.tsv')

# Filtering metadata file to use only samples where we have mutation information
metadata = metadata %>%
    filter(Sample.ID %in% maf_sp$Tumor_Sample_Barcode)

We are going to consider the main four subgroups for colon cancer, annotated as COAD_CIN, COAD_GS, COAD_MSI, and COAD_POLE. To obtain the average mutational profile for the COAD_CIN subgroup you can use the following code:

# Get samples from group
samples_group = metadata %>%
    filter(Subtype == 'COAD_CIN') %>%
    pull(Sample.ID)

# Select group samples from main matrix and get average
mm_group = rowMeans(relative_mut_matrix[,samples_group])
mm_group = data.frame(mm_group)

# Add row names as column and print
mm_group_to_print = cbind(rownames(mm_group),mm_group)
colnames(mm_group_to_print) = c('MutationType', 'COAD_CIN')
write.table(mm_group_to_print,
            'signatures/avg_COAD_CIN.SBS96.all',
            quote = F, row.names = F, sep = '\t')

# Plot average mutational profiles (note the percentage parameter now)
plotSBS(matrix_path = 'signatures/avg_COAD_CIN.SBS96.all',
        output_path = 'signatures/',
        project = 'avg_COAD_CIN.SBS96.all',
        plot_type = '96',
        percentage=TRUE)

Discussion #2

Please explore the provided metadata from cBioPortal and answer the questions below.

• Q4. How many samples are included in each of the subgroups?
  COAD_CIN:
  COAD_GS:
  COAD_MSI:
  COAD_POLE:

• Q5. Generate the average mutational profile plots for the COAD_GS, COAD_MSI, and COAD_POLE subgroups, and compare them agains the one previously done for COAD_CIN.

Hint

These are the expected average mutational profiles for the two indicated subgroups. Do you observe any differences with the one shown above for the COAD_CIN subgroup?

5. Extracting mutational signatures

In order to perform extraction of mutational signatures, we are going to use SigProfilerExtractor, the associated tool from the SigProfiler suite. We will use it to extract de novo mutational signatures from a set of samples and decompose the de novo extracted signatures into COSMIC reference signatures.

Side-note: It is also possible to run SigProfilerExtractor directly from MAF (or VCF) files, without generating a mutational matrix first. In this case, we need to change the input_type parameter to vcf. What is going to happen is that SigProfilerExtractor will call SigProfilerMatrixGenerator internally, and perform the de novo signature extraction after the matrix generation has been finalized. However, when working with a novel dataset, it is always a good practice to first generate mutational matrices and also visualize the mutational profiles to check if there are any particular issues with your data before performing more advanced analysis.

To load the package you can use:

library(SigProfilerExtractorR)

## 
## Attaching package: 'SigProfilerExtractorR'

## The following object is masked from 'package:SigProfilerMatrixGeneratorR':
## 
##     install

To run SigProfilerExtractor in our colorectal cancer cohort you can use the code below:

Side-note: We are setting nmf_replicates = 3 and maximum_signatures = 3 to decrease how long the command takes to run. For accurate results, you will need to increase those parameters (e.g., the default and recommended values are nmf_replicates = 100 and maximum_signatures = 25). In particular, the maximum number of signatures will be dependent of the total number of samples in the cohort, and the average number of mutations per sample.

sigprofilerextractor(input_type = 'matrix',
                     output = 'signatures/SPE/',
                     input_data = 'signatures/SPMG/output/SBS/COREAD.SBS96.exome',
                     nmf_replicates = 3,
                     minimum_signatures = 1,
                     maximum_signatures = 3,
                     exome = T)

A successful run will be finished by the following message:

Your Job Is Successfully Completed! Thank You For Using SigProfilerExtractor.

Discussion #3

Please explore the SigProfilerExtractor results and answer the questions below

• Q6. How many signatures have been selected as the optimal number for our colorectal cancer cohort?

• Q7. Do you think this result would be the same as the one obtained using the default (and recommended) parameters for SigProfilerExtractor? Yes No Why could be the cause for this (considering what has been presented in the lecture)? If you have some time feel free to run

• Q8. Do any of the de novo extracted signatures remind you of any of the average mutational profiles obtained earlier? Do you think certain subtypes could be enriched in specific signatures?

6. COSMIC reference mutational signatures

The COSMIC reference set of mutational signatures was derived after the analysis of many thousands of DNA sequenced samples from various cancer types by large international consortia over the last decade (Alexandrov et al. 2013 Nature and Alexandrov et al. 2020 Nature).

These reference mutational signatures have been associated with specific environmental exposures, lifestyle choices, and endogenous cellular mechanisms. More information about the etiologies of the signatures and the cancer types where they have previously been found is available at the COSMIC Mutational Signatures website.

For our analysis, we are focusing on the SBS96 mutational context. In this regard, all the extracted reference signatures to date can be found here.

Discussion #4

Please explore the COSMIC Mutational Signatures website and answer the questions below:

• Q9. How many different variant classes are currently available in the COSMIC Mutational Signatures catalogue?

• Q10. The reference mutational signatures have been renormalized to different genomes. How many different species are currently supported for the SBS COSMIC reference signatures?

• Q11. In the SigProfilerExtractor results, one of the directories showed the de novo signatures decomposed into a series of COSMIC signatures. Which is the etiology of the COSMIC signature corresponding to the highest contribution to the de novo extracted signatures SBS96A? Aging Clock-like Tobacco smoking UV light exposure Defective mismatch repair / MSI POLE mutation

• Q12. Which is the proposed etiology for the COSMIC signatures mostly reconstructing SBS96B? Aging Clock-like Tobacco smoking UV light exposure Defective mismatch repair / MSI POLE mutation

7. Assigning reference mutational signatures

Leveraging the COSMIC mutational signatures, we will perform a mutational signature assignment analysis to quantify the number of mutations contributed by each signature to a given cancer sample and, therefore, decipher which mutational processes have been active in each individual tumor. To do this, we will make use of SigProfilerAssignment, the most recent tool of the SigProfiler suite.

In this case, in contrast to the de novo extraction of signatures, we need to provide to SigProfilerAssignment not only the somatic mutations (in the form of a MAF/VCF, or directly using a mutational matrix), but also a set of reference mutational signatures. The COSMIC Mutational Signatures, that we have just reviewed are used as default, but any other set of reference signatures can be used by providing the corresponding signature matrix using the signature_database parameter. A signature matrix should contain as many columns as reference signatures, and as many rows as the specific mutational context (96 in the case of our preferred SBS96 mutational context).

To assign reference signatures to individual samples using SigProfilerAssignment, you can use the code below:

# Load library
library(SigProfilerAssignmentR)

# Run assignment analysis
cosmic_fit(samples = 'signatures/SPMG/output/SBS/COREAD.SBS96.exome',
           output = 'signatures/SPA',
           input_type='matrix',
           exome = T)

8. Downstream analysis of signature assignment results

8.1 Assessing the accuracy of the signature activities

Assigning mutational signatures is a mathematical optimization problem, which consists in obtaining the best combination of reference signatures that better reconstruct the original mutational profile. Considering this, it is a good practice to check how good is this reconstruction, because it can happen that even if the method tries to optimize the reconstruction, this is still not good enough. Different similarity measures can be used to check this. The most common in the mutational signatures field is the cosine similarity, although others such as correlation, L1/L2 norm, or KL divergence can be used. A cosine similarity above 0.90 commonly indicates a good reconstruction.

To check the cosine similarity of the reconstruction for some specific samples, we can check the Assignment_Solution_Samples_Stats.txt file, which is inside the directory data/SPA/Assignment_Solution/Solution_Stats. We can do that in R, and generate some useful visualizations:

stats = read.delim('signatures/SPA/Assignment_Solution/Solution_Stats/Assignment_Solution_Samples_Stats.txt')

ggplot(stats) +
    aes(x=Cosine.Similarity) +
    labs(x='')+
    geom_histogram(aes(y = after_stat(density))) +
    geom_density(col = 4, lwd = 1.5) +
    geom_vline(aes(xintercept = 0.9),
               col = 2, lwd = 1.5) +
    labs(x = 'Cosine Similarity') +
    theme_bw()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

8.2 Visualizing signature activities from SigProfilerAssignment

To visualize the mutational signature assignment results, we will check the plots provided by SigProfilerAssignment (available at signatures/SPA/Assignment_Solution/Activities/).

The TMB plot shows the tumor mutational burden for each signature in each sample. Each dot represents an individual sample and only samples where the signature is found are shown. The numbers below the dots for each cancer type indicate the number of tumours in which mutations were attributed to the signature (above the blue horizontal line) and the total number of tumours analyzed (below the blue horizontal line).

As we can see, a lot of different signatures have been attributed to our colorectal cancer samples, which is commonly a sign of overfitting and poor performance of the method. In this case, especially when we have a large cohort of sample, it is much more benefitial to use the activities generated from signature extraction and then decomposition to COSMIC signatures. This is because the number of COSMIC signatures will be reduced and the assignment process will be more accurate than directly using SigProfilerAssignment.

On the other hand, SigProfilerAssignment it’s really useful in the case that you only have one or a few samples available, because in this scenario de novo extraction of signatures is not possible, since it requires a large number of samples (and mutations).

8.3 Visualizing signature activities from SigProfilerExtractor

Considering what we discussed, we are going to analyze a bit more in detail the signature activities provided by SigProfilerExtractor. It’s important to keep in mind that SigProfilerExtractor is running SigProfilerAssignment automatically for us. However, in this case, the assignment of signatures to samples is done using the de novo extracted signatures, as well as the COSMIC decomposition of these de novo signatures.

We can start by reviewing a similar TMB plot, which now looks quite different.

We will now generate custom visualizations using ggplot2 and the activities output matrix from the mutational signature assignment analysis.

# Read activities matrix
acts = read.delim('signatures/SPE/SBS96/Suggested_Solution/COSMIC_SBS96_Decomposed_Solution/Activities/COSMIC_SBS96_Activities.txt')

We can first calculate and plot the average number of mutations contributed by every signatures across the cohort of 528 samples.

# Calculate average activities per signature
avg_acts = colMeans(acts[,-1])
avg_acts

##      SBS1      SBS5    SBS10a    SBS10b     SBS15     SBS28     SBS40     SBS44 
## 118.66288 133.80114  63.38068  61.90341  34.05682  17.34470  36.36174  68.67992

# Visualize average activities per signature
barplot(avg_acts)

We can also plot the activities for every sample. In this case we are going to plot the relative activities. If we want to plot the absolute activites we need to change the position parameter in geom_bar to position=stack.

# Reformat dataframe to use ggplot
# (more info here: https://tidyr.tidyverse.org/reference/pivot_longer.html)
acts_tidy = acts %>%
    pivot_longer(cols = !Samples,
                 names_to = 'Signature',
                 values_to = 'Mutations')

# Generate stacked barplot (percent stacked)
ggplot(acts_tidy) +
    aes(x = Samples, y = Mutations, fill = Signature) +
    geom_bar(position = 'fill', stat = 'identity') +
    theme_bw() +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank())

Since we have a lot of samples, the plot above is quite messy and not very useful. We will group the samples according to the metadata groups in the next section, but for now we can plot for example the 10 most mutated cases.

# Calculate number of mutations per sample
number_of_mutations = rowSums(acts[,-1])

# Selecting the activities of only the top 10 mutated cases
top_10_mutated_samples = acts[order(number_of_mutations,
                                    decreasing = T)[1:10],]

# Reformatting and plotting
top_10_mutated_samples %>%
     pivot_longer(cols = !Samples,
                 names_to = 'Signature',
                 values_to = 'Mutations') %>%
    ggplot() +
    aes(x = reorder(Samples, Mutations), y = Mutations, fill = Signature) +
    geom_bar(position = 'fill', stat = 'identity') +
    theme_bw() +
    labs(x = 'Samples')  +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank())

8.4 Associating signature activities with specific metadata

As indicating above, common signature analysis in large cohorts of patients include an analysis considering the underlying metadata, in order to answer any specific hypothesis that we may have about the data. In this case, and considering we observed some differences in the mutational profiles according to the molecular subgroups, it would be great to know if there are differences in the signatures assigned to tumor cases from different subgroups.

To do this, we can, for example, calculate the average signature activities per subgroup. This will require a bit of work with the tidyverse collection of packages, which you can see in the code below.

# Merge activities and metadata tables
# (The samples column needs to be renamed in one of them)
acts_and_metadata = acts_tidy %>%
    rename(Sample.ID = Samples) %>%
    left_join(metadata)

## Joining with `by = join_by(Sample.ID)`

# Calculate average activities per subtype
acts_per_subgroup = acts_and_metadata %>%
    group_by(Subtype, Signature) %>%
    summarise(Avg_mutations = mean(Mutations))

## `summarise()` has grouped output by 'Subtype'. You can override using the
## `.groups` argument.

head(acts_per_subgroup)

# Selecting only COAD subtypes
acts_per_subgroup = acts_per_subgroup %>%
    filter(grepl('COAD', Subtype))

# Plotting stacked barplot per subtype
ggplot(acts_per_subgroup) +
    aes(x = reorder(Subtype, Avg_mutations), y = Avg_mutations, fill = Signature) +
    geom_bar(position = 'fill', stat = 'identity') +
    theme_bw() +
    labs(x = 'Colon Cancer Subtype',
         y = 'Average number of mutations')

Discussion #5

Explore the main signatures for each of the subtypes and answer the following questions:

• Q13. Which are the most prevalent signatures in the COAD_POLE subtype?
  Aging signatures (SBS1, SBS5) MSI signatures (SBS15, SBS44) POLE signatures (SBS10a, SBS10b)
• Q14. According to the signature results, which is the DNA repair associated gene that seems to be mutated and causing the mutational pattern observed in the COAD_POLE cases?
• Q15. Which set of signatures is more prevalent in the COAD_MSI subtype in comparison to others?
  Aging signatures (SBS1, SBS5) MSI signatures (SBS15, SBS44) POLE signatures (SBS10a, SBS10b)
• Q16. According to the signature results, which is the DNA repair pathway that seems to be altered and causing the mutational pattern observed in the COAD_MSI cases?
  Homologous recombination Mismatch repair Base excision repair Nucleotide excision repair

9. [OPTIONAL] Advanced mutational signature analysis using the SigProfilerAssignment python package

In order to install the SigProfiler suite of tools in your own computer, first, you will need to install the Miniconda package manager, which is highly recommended for python users and can also be used to manage your R dependencies. Miniconda philosophy is built around conda environments, which correspond to closed containers where all the versions of the different python/R packages are fixed. This structure highly benefits the reproducibility of the code, as each analysis can be done with an exclusively dedicated environment, which will not change any dependency version, even though the general python installation of the computer or a particular package is updated. Please note that Miniconda installation differs between macOS and Windows users.

Following the code below, you can install the main SigProfiler packages for both python and R, for using them in your own analysis. Instructions for both macOS and Windows are provided, and analogous steps could be taken in Unix-based computers.

# Install Miniconda (more info here: https://docs.conda.io/projects/miniconda/en/latest/)
## macOS (Intel)
system('wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh')
system('bash Miniconda3-latest-MacOSX-x86_64.sh')

## macOS (Apple M1)
system('wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh')
system('bash Miniconda3-latest-MacOSX-arm64.sh')

## Windows
# Download https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe

After downloading and installing miniconda, we are going to create a new environment that we will call mutational_signatures, and then we will install python 3.10 inside the environment.

# Install python
## macOS
new_environment_name = 'mutational_signatures'
path_of_miniconda = ' /Users/mdiazgay/opt/miniconda3/' # Should be changed
path_of_conda = paste0(path_of_miniconda, '/bin/conda')
system(paste0(path_of_conda, ' create --name ', new_environment_name, ' -y'))
system(paste0(path_of_conda, ' install -n ', new_environment_name, ' python=3.10 -y'))

## Windows (using Anaconda Prompt)
conda create --name bimm143_wi23 -y
conda activate bimm143_wi23
conda install python=3.10 -y

# Install SigProfilerAssigment python package
## macOS
system(paste0(path_of_miniconda, '/envs/', new_environment_name,
              '/bin/pip install SigProfilerAssignment'))

## Windows (using Anaconda Prompt)
conda activate bimm143_wi23
pip install SigProfilerAssignment

Lastly, we will install all the R packages needed, including reticulate, which is required to use python packages in R; devtools, which is needed to install other packages directly from GitHub; and all the R wrappers for the different SigProfiler tools.

# Install reticulate (package connecting python & R)
if (!require("reticulate")){
    install.packages("reticulate")
}

# Install R wrapper (SigProfilerAssignmentR)
if (!require("devtools")){
    install.packages("devtools")
}

# Install SigProfiler tools and additional utilities
if (!require("SigProfilerMatrixGeneratorR")){
    devtools::install_github("AlexandrovLab/SigProfilerMatrixGeneratorR")
}

if (!require("SigProfilerAssignmentR")){
    devtools::install_github("AlexandrovLab/SigProfilerAssignmentR")
}

if (!require("SigProfilerExtractorR")){
    devtools::install_github("AlexandrovLab/SigProfilerExtractorR")
}

if (!require("SigProfilerPlottingR")){
    devtools::install_github("AlexandrovLab/SigProfilerPlottingR")
}

About this document

Here we use the sessionInfo() function to report on our R system setup at the time of document execution.

sessionInfo()

## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] SigProfilerExtractorR_1.1.16       SigProfilerPlottingR_0.1.0        
##  [3] SigProfilerMatrixGeneratorR_1.2.13 reticulate_1.30                   
##  [5] lubridate_1.9.2                    forcats_1.0.0                     
##  [7] stringr_1.5.0                      dplyr_1.1.3                       
##  [9] purrr_1.0.2                        readr_2.1.4                       
## [11] tidyr_1.3.0                        tibble_3.2.1                      
## [13] ggplot2_3.4.4                      tidyverse_2.0.0                   
## [15] labsheet_0.1.2                    
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.5        pkgload_1.3.2     jsonlite_1.8.5    here_1.0.1       
##  [5] bslib_0.4.2       shiny_1.7.4       highr_0.10        yaml_2.3.7       
##  [9] remotes_2.4.2.1   sessioninfo_1.2.2 pillar_1.9.0      lattice_0.20-45  
## [13] glue_1.6.2        digest_0.6.31     promises_1.2.0.1  colorspace_2.1-0 
## [17] htmltools_0.5.5   httpuv_1.6.9      Matrix_1.5-3      pkgconfig_2.0.3  
## [21] devtools_2.4.5    xtable_1.8-4      scales_1.2.1      processx_3.8.1   
## [25] later_1.3.0       tzdb_0.4.0        timechange_0.2.0  farver_2.1.1     
## [29] generics_0.1.3    usethis_2.1.6     ellipsis_0.3.2    cachem_1.0.7     
## [33] withr_2.5.0       cli_3.6.1         magrittr_2.0.3    crayon_1.5.2     
## [37] mime_0.12         memoise_2.0.1     evaluate_0.20     ps_1.7.5         
## [41] fs_1.6.1          fansi_1.0.4       pkgbuild_1.4.0    profvis_0.3.8    
## [45] tools_4.2.3       prettyunits_1.1.1 hms_1.1.3         lifecycle_1.0.3  
## [49] munsell_0.5.0     callr_3.7.3       compiler_4.2.3    jquerylib_0.1.4  
## [53] rlang_1.1.1       grid_4.2.3        rstudioapi_0.15.0 htmlwidgets_1.6.2
## [57] miniUI_0.1.1.1    labeling_0.4.2    rmarkdown_2.21    gtable_0.3.3     
## [61] codetools_0.2-19  R6_2.5.1          knitr_1.42        fastmap_1.1.1    
## [65] utf8_1.2.3        rprojroot_2.0.3   stringi_1.7.12    Rcpp_1.0.10      
## [69] vctrs_0.6.4       png_0.1-8         urlchecker_1.0.1  tidyselect_1.2.0 
## [73] xfun_0.38

 

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